Underfitting is what happens when an AI model is too basic to capture meaningful patterns in your data. It leads to wildly inaccurate predictions, low ROI, and frustrated teams wondering why their 'smart tech' isn't so smart.
Underfitting occurs when an artificial intelligence or machine learning model is too simplistic to understand the underlying patterns in your data. The model essentially shrugs and says, "I don’t get it," leading to inaccurate outputs that aren’t helpful to the business. This usually happens when the model is poorly trained, uses too few features, or has been constrained to avoid 'overcomplicating' things.
In short: your AI isn’t paying enough attention. It’s trying to solve a business problem using half the story—and the results show it.
Underfitting kills ROI before you even know there’s a problem. If your AI model can't learn from your company data—whether that’s customer behavior, case histories, or marketing performance—it produces overly generic outputs or misses clear business signals. From automated lead scoring to chatbots to content generators, poorly performing models slow things down and make teams distrust the tech (rightfully so).
Let’s put a number to the risk: According to a 2023 IBM survey, 64% of companies reported improved productivity from AI—but that leaves 36% still spinning their wheels. Errors like underfitting—where models don’t learn enough to be useful—are a common culprit. In fact, a 2023 Gartner report found that 41% of organizations deploying AI saw adverse outcomes, often due to poor oversight, including underperforming models that were never properly tuned.
Here’s a common scenario we see with marketing teams in service-based businesses, especially in B2B or SaaS:
A team launches an AI-powered tool to generate blog content from customer FAQs. Smart idea—except they feed the AI only top-level questions (like "What is a CRM?") and keep the model settings ultra-conservative to avoid hallucinations. The result? Every post reads like a Wikipedia stub, lacking specificity, brand voice, or actual buyer relevance. It checks boxes but drives zero traffic or conversions.
What went wrong:
How this could be improved:
Result? Content goes from “meh” to meaningful. Websites rank better. Leads read the content and actually convert. And the AI becomes an asset—one that actually learns and improves.
At Timebender, we help teams fix the root causes of underfitting by training AI models the right way—and teaching your team how to give them the signals they need to succeed. From marketing and CRM automations to onboarding flows and legal intake, we build workflows where AI doesn't just show up—it actually does the job you hired it for.
Our clients don’t need model theory. They need to know why their AI sucks at qualifying leads, or why it’s writing blog posts that sound like they belong in 2009.
Want better results from your AI systems? Book a Workflow Optimization Session and let’s fix the model—not just the symptoms.
Gartner 2023 AI Governance Report
